Criteria for the use of regression analysis for remote sensing of sediment and pollutants

Whitlock, C. H.; Kuo, Chin Y.; Lecroy, S. R.

doi:10.1016/0034-4257(82)90035-9

Cited by 51 publications

(10 citation statements)

References 2 publications

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“…In past years investigators have found positive correlations between suspended sediment concentration and re¯ectance values recorded by radiometers in the visible and near-infrared regions of the spectrum (Blanchard and Leamer 1973, Merry 1977, Duggin 1980, Whitlock et al 1982 Schiebe 1986, Novo et al 1989 and Choubey 1990 ). examined the e ect of sediment type on the relation between re¯ectance and suspended sediment concentration.…”

Section: Introductionmentioning

confidence: 94%

Laboratory experiment, field and remotely sensed data analysis for the assessment of suspended solids concentration and secchi depth of the reservoir surface water

Choubey¹

1998

International Journal of Remote Sensing

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Section: Introductionmentioning

confidence: 94%

Laboratory experiment, field and remotely sensed data analysis for the assessment of suspended solids concentration and secchi depth of the reservoir surface water

Choubey¹

1998

International Journal of Remote Sensing

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“…Despite these problems, detailed laboratory studies have provided useful results (Scherz eial. 1969, Scherz 1972, Moore 1977, Whitlock et al 1982) that complement the necessarily less detailed field studies (Bartolucci et al 1977, Holyer 1978. For instance, such laboratory studies have clearly demonstrated positive but asymptotic relationships between SSC and reflectance: the influence of bottom reflectance when SSC is less than 10 mg/1 and the long wavelength shift in the start of the reflectance asymptote at high leveis of SSC.…”

Section: Int-roductionmentioning

confidence: 92%

The effect of viewing geometry and wavelength on the relationship between reflectance and suspended sediment concentration

Novo

Hansom

Curran

1989

International Journal of Remote Sensing

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Abstract. The relationship between reflectance and suspended sediment concentration (SSC) is depcndent upon the wavebands sensed and the vicwing geometry of the sensor. The laboratory experiment reported in this paper investigatcd thesc dependenecs. The retlectance in four wavebands (two visible and two near-infrared) of a large water-filled and wave-free tank were recorded as three variables were changed, namely SSC. sensor viewing angle and relative sensor azimuth. The strength of the positive relationship between reflectance and SSC was shown to vary with (i) wavelength, as the maximum reflectance and SSC class separability occurred at the longer visible wavelengths, and (ii) viewing geometry, as the reflectance/SSC asymptote was dependent upon both the angle and azimuth of the sensor. The optimum conditions for the sensing of SSC ~c concluded to be nadir in visible (0-55 and 0.65 pm) and ne,ar-infrared (0-75pm) wavelengths.

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“…Most bio-optical algorithms use simple ratios of reflectance in blue and green bands or combinations of ratios as parameters for regression analysis (Gordon & Morel, 1983;Whitlock, Kuo & LeCroy, 1982;Lathrop & Lillsand, 1986;Tassan, 1993;Pattiaratchi, Lavery, Wyllie & Hick, 1994). However, the relation between radiance data and Chl are complex and nonlinear.…”

Section: Introductionmentioning

confidence: 98%

Application of a multilayer perceptron neural network to phytoplankton concentration using marine reflectance measures

Kuo

2005

Active and Passive Remote Sensing of the Oceans

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The multilayer perceptron (MLP) neural network have been widely used to fit non-linear transfer function and performed well. In this study, we use MLP to estimate chlorophyll-a concentrations from marine reflectance measures. The optical data were assembled from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Algorithm Mini-workshop (SeaBAM). Most bio-optical algorithms use simple ratios of reflectance in blue and green bands or combinations of ratios as parameters for regression analysis. Regression analysis has limitations for nonlinear function. Neural network, however, have been shown better performance for nonlinear problems. The result showed that accuracy of chlorophyll-a concentration using MLP is much higher than that of regression method. Nevertheless, using all of the five bands as input can derive the best performance. The results showed that each band could carry some useful messages for ocean color remote sensing. Only using band ratio (OC2) or band switch (OC4) might lose some available information. By preprocessing reflectance data with the principle component analysis (PCA), MLP could derive much better accuracy than traditional methods. The result showed that the reflectance of all bands should not be ignored for deriving the chlorophyll-a concentration because each band carries different useful ocean color information.

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Criteria for the use of regression analysis for remote sensing of sediment and pollutants

Cited by 51 publications

References 2 publications

Laboratory experiment, field and remotely sensed data analysis for the assessment of suspended solids concentration and secchi depth of the reservoir surface water

Laboratory experiment, field and remotely sensed data analysis for the assessment of suspended solids concentration and secchi depth of the reservoir surface water

The effect of viewing geometry and wavelength on the relationship between reflectance and suspended sediment concentration

Application of a multilayer perceptron neural network to phytoplankton concentration using marine reflectance measures

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